Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly

Journal of Financial Econometrics (2020, forthcoming) and University of Zurich, Department of Economics, Working Paper No. 290, Revised version

28 Pages Posted: 12 Jun 2018 Last revised: 14 Oct 2020

See all articles by Gianluca De Nard

Gianluca De Nard

University of Zurich - Department of Banking and Finance

Olivier Ledoit

University of Zurich - Department of Economics

Michael Wolf

University of Zurich - Department of Economics

Date Written: December 1, 2018

Abstract

This paper injects factor structure into the estimation of time-varying, large-dimensional covariance matrices of stock returns. Existing factor models struggle to model the covariance matrix of residuals in the presence of time-varying conditional heteroskedasticity in large universes. Conversely, rotation-equivariant estimators of large-dimensional time-varying covariance matrices forsake directional information embedded in market-wide risk factors. We introduce a new covariance matrix estimator that blends factor structure with time-varying conditional heteroskedasticity of residuals in large dimensions up to 1000 stocks. It displays superior all-around performance on historical data against a variety of state-of-the-art competitors, including static factor models, exogenous factor models, sparsity-based models, and structure-free dynamic models. This new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of stock returns.

Keywords: Dynamic conditional correlations, factor models, multivariate GARCH, Markowitz portfolio selection, nonlinear shrinkage

JEL Classification: C13, C58, G11

Suggested Citation

De Nard, Gianluca and Ledoit, Olivier and Wolf, Michael, Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly (December 1, 2018). Journal of Financial Econometrics (2020, forthcoming) and University of Zurich, Department of Economics, Working Paper No. 290, Revised version, Available at SSRN: https://ssrn.com/abstract=3194492 or http://dx.doi.org/10.2139/ssrn.3194492

Gianluca De Nard (Contact Author)

University of Zurich - Department of Banking and Finance ( email )

Zürichbergstrasse 14
Zürich, Zürich CH-8032
Switzerland

HOME PAGE: http://denard.ch

Olivier Ledoit

University of Zurich - Department of Economics ( email )

Wilfriedstrasse 6
Zürich, 8032
Switzerland

Michael Wolf

University of Zurich - Department of Economics ( email )

Wilfriedstrasse 6
Zurich, 8032
Switzerland

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